Volume 37 Issue 2
Feb.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Aerospace Power  > 2022  >  37(2): 409-423
XIANG Dewei, ZHENG Qiangang, ZHANG Haibo, CHEN Cheng, FANG Juan. Aero-engine on-board adaptive steady-state model base on NN-PSM[J]. Journal of Aerospace Power, 2022, 37(2): 409-423. doi: 10.13224/j.cnki.jasp.20210138
Citation: XIANG Dewei, ZHENG Qiangang, ZHANG Haibo, CHEN Cheng, FANG Juan. Aero-engine on-board adaptive steady-state model base on NN-PSM[J]. Journal of Aerospace Power, 2022, 37(2): 409-423. doi: 10.13224/j.cnki.jasp.20210138
shu

Aero-engine on-board adaptive steady-state model base on NN-PSM

doi: 10.13224/j.cnki.jasp.20210138

  • Jiangsu Province Key Laboratory of Aerospace Power System，College of Energy and Power Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，China

  • Received Date: 2021-04-03
  • Publish Date: 2022-02-28
    Abstract
  • In order to establish a high-precision，high-real-time aero-engine on-board adaptive steady-state model suitable for large envelopes and multiple states，a on-board adaptive steady-state model based on neural network and propulsion system matrix fusion （NN-PSM） was proposed.Adaptive steady-state modeling method was based on small deviation linearization method to linearize the engine to extract the propulsion system matrix，which was used to characterize output deviation.The engine baseline model was established based on the neural network，and the relationship between the flight conditions and the engine output was mapped，and the neural network used the strong fitting ability to improve the steady-state accuracy of the on-board model.The Kalman filter was designed in real time to improve the adaptive ability of the model.The simulation was carried out under large envelope and variable state flight conditions，and compared with the traditional compact propulsion system model （CPSM） model.The results showed that the average accuracy of the NN-PSM model was within 0.66%，while the average accuracy of the CPSM model was about 2.07%；the time was about one-tenth of the CPSM model，and the amount of data storage was small.

     

    • FullText(HTML)
  • loading
    • References(32)
  • [1]
    孙健国.面向21世纪航空动力控制展望[J].航空动力学报，2001，16（2）:97-102.
    [2]
    袁鸯.发动机自适应建模及神经网络控制[D].南京:南京航空航天大学，2004.
    [3]
    何凤林.变循环发动机直接推力控制方法研究[D].南京:南京航空航天大学，2019.
    [4]
    袁春飞，姚华，刘源.基于机载自适应模型的航空发动机控制[J].推进技术，2006，27（4）:354-358.
    [5]
    SILVA V V R，KHATIB W，FLEMING P J.Performance optimization of gas turbine engine[J].Engineering Application of Artificial Intelligence，2005，18（5）:575-583.
    [6]
    SIMON D L，RINEHART A W.A model-basted anomaly detection approach for analyzing streaming aircraft engine measurement data[R].ASME Paper GT2014-27172，2014.
    [7]
    RINEHART A W，SIMON D L.An integrated architecture for aircraft engine performance monitoring and fault diagnostics:engine test result[R].AIAA 2014-3924，2014.
    [8]
    MAY R D，CSANK J，LAVELLE T M，et al.A high-fidelity simulation of a generic commercial aircraft engine and controller[R].AIAA 2010-6630，2010.
    [9]
    GILYARD G，ORME J.Subsonic flight test evaluation of a performance seeking control algorithm on an F-15 air-plane[R].AIAA 92-1821，1992.
    [10]
    LIN K M，LIN C J.A study on reduced support vector machines[J].IEEE Transsactions on Neural Networks，2003，14（6）:1449-1459.
    [11]
    VLOPONI A.Enhanced self-tuning on-board real-time model （ESTROM） for aircraft engine performance health tracking[R].NASA/CR 2008-215272，2008.
    [12]
    SIMON D L.An integrated architecture for onboard aircraft engine performance trend monitoring and gas path fault diagnostics[R].NASA TM 2010-216358，2010.
    [13]
    李业波，李秋红，黄向华，等.航空发动机气路部件故障融合诊断方法研究[J].航空学报，2014，35（6）:1612-1622.
    [14]
    胡鹤翔.基于改进SVM的航空发动机齐鲁故障诊断研究[D].天津:中国民航大学，2019.
    [15]
    程都.基于神经网络的航空发动机模型自适应修正[D].辽宁 大连:大连理工大学，2019.
    [16]
    王健康，张海波，孙健国，等.基于复合模型及FSQP算法的发动机性能寻优控制试验[J].推进技术，2012，33（8）:579-590.
    [17]
    鲁峰，黄金泉，吕怡秋，等.基于非线性自适应滤波的发动机气路部件健康诊断方法[J].航空学报，2013，34（11）:2529-2538.
    [18]
    鲁峰，黄金泉，陈煜.航空发动机部件性能故障融合诊断方法研究[J].航空动力学报，2009，24（7）:1649-1653.
    [19]
    王海泉，欧阳玲，黄杰.涡扇发动机机载自适应模型及性能蜕化估计[J].计算机仿真，2012，29（10）:76-80.
    [20]
    王修岩，李萃芳，高铭阳，等.基于SVM和SNN的航空发动机气路故障诊断[J].航空动力学报，2014，29（10）:2493-2498.
    [21]
    皮骏，马圣，张奇奇，等.基于改进果蝇算法优化的GRNN航空发动机排期温度预测模型[J].航空动力学报，2019，34（1）:9-15.
    [22]
    李永进，张海波，张天宏.一种考虑非线性余项的机载发动机自适应模型建立及其在寻优控制中的应用[J].推进技术，2016，37（1）:172-180.
    [23]
    李永进，徐植桂，张海波.一种超声速状态下机载推进系统模型的建模方法[J].航空动力学报，2017，32（5）:1273-1279.
    [24]
    丁凯峰，樊思齐.基于RBF网络的航空发动机辨识模型[J].航空动力学报，2000，15（2）:205-208.
    [25]
    ORME J S，GILYARD G B.Preliminary supersonic flight test evaluation of performance seeking control[R].AIAA 93-1821，1993.
    [26]
    李永进，贾爽龙，张海波，等.基于稀疏编码器的发动机机载模型建模方法研究[J].推进技术，2017，38（6）:1209-1217.
    [27]
    MAINE T，GILYARD G，LAMBERT H.A preliminary evaluation of an F100 engine parameter estimation process using flight data[R].Orlando，US:26th Joint Propulsion Conference，1990.
    [28]
    NOBBS S，JACOBS S，DONAHUE D.Deveiopment of the full-envelop performance seeking control algorithm[R].Nashville，US:28th Joint Propulsion Conference and Exhibit，1992.
    [29]
    ZHENG Qiangang，WANG Yong.Aero-engine dynamic model based on an improved compact propulsion system dynamic model[J].Journal of Systems and Control Engineering，2020，235（7）:1036-1045.
    [30]
    金崇文，郑前钢，张海波，等．基于复合推进系统动态模型-状态变量模型的航空发动机直接推力预测控制[J].推进技术，2022，43（1）:348-357.
    [31]
    AZGHADI S M R，SHAHHOSSEINI H.Gender classification based on feed forward back propagation neural network.[C]∥International Conference on Artificial Intelligence and Innovations:From Theory to Applications.Athens,Greece:NASA,2007:299-304.
    [32]
    王嵘冰，徐艳红，李波，等.BP神经网络隐含层节点数确定方法研究[J].计算机技术与发展，2018，28（4）:31-35.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF(7281.0KB)
    XML

    Article Metrics

    Article views (752) PDF downloads(108) Cited by()
    Proportional views
    Related

    Copyright © 《Journal of Aerospace Power》 All Rights Reserved.

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return